{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">注</font>: 使用 tensorboard 可视化需要安装 tensorflow (TensorBoard依赖于tensorflow库，可以任意安装tensorflow的gpu/cpu版本)\n",
    "\n",
    "```shell\n",
    "pip install tensorflow-cpu\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-22T07:01:08.110069700Z",
     "start_time": "2024-07-22T07:01:02.910328800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.9.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.2\n",
      "sklearn 1.5.0\n",
      "torch 2.3.1+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-22T07:02:43.406684400Z",
     "start_time": "2024-07-22T07:02:41.081469900Z"
    }
   },
   "outputs": [],
   "source": [
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "# fashion_mnist图像分类数据集\n",
    "train_ds = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "test_ds = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "# torchvision 数据集里没有提供训练集和验证集的划分\n",
    "# 这里用 random_split 按照 11 : 1 的比例来划分数据集\n",
    "train_ds, val_ds = random_split(train_ds, [55000, 5000], torch.Generator().manual_seed(seed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-22T07:02:48.457107200Z",
     "start_time": "2024-07-22T07:02:48.454108800Z"
    }
   },
   "outputs": [],
   "source": [
    "from torchvision.transforms import Normalize\n",
    "\n",
    "# 遍历train_ds得到每张图片，计算每个通道的均值和方差\n",
    "def cal_mean_std(ds):\n",
    "    mean = 0.\n",
    "    std = 0.\n",
    "    for img, _ in ds:\n",
    "        mean += img.mean(dim=(1, 2))\n",
    "        std += img.std(dim=(1, 2))\n",
    "    mean /= len(ds)\n",
    "    std /= len(ds)\n",
    "    return mean, std\n",
    "\n",
    "\n",
    "# print(cal_mean_std(train_ds))\n",
    "# 0.2860， 0.3205\n",
    "transforms = nn.Sequential(\n",
    "    Normalize([0.2856], [0.3202])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-22T07:02:53.066994600Z",
     "start_time": "2024-07-22T07:02:53.059987700Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data.dataloader import DataLoader\n",
    "\n",
    "batch_size = 32\n",
    "# 从数据集到dataloader\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=4)\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False, num_workers=4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型\n",
    "\n",
    "使用深度可分离的卷积\n",
    "\n",
    "pytorch需要自行实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-22T07:08:41.093283Z",
     "start_time": "2024-07-22T07:08:41.088284500Z"
    }
   },
   "outputs": [],
   "source": [
    "# 定义深度可分离卷积层，torch没有实现，tf有实现\n",
    "class DepthWiseConv2d(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True):\n",
    "        super(DepthWiseConv2d, self).__init__() #这里写为super().__init__()，等价的\n",
    "        self.depthwise_conv = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, groups=in_channels, bias=False) #groups参数表示一个卷积核的每个通道分别进行运算\n",
    "        self.pointwise_conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0, bias=bias)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.depthwise_conv(x)\n",
    "        x = self.pointwise_conv(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-22T07:15:25.299460800Z",
     "start_time": "2024-07-22T07:15:25.291903800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.weight\tparamerters num: 288\n",
      "conv1.bias\tparamerters num: 32\n",
      "conv2.depthwise_conv.weight\tparamerters num: 288\n",
      "conv2.pointwise_conv.weight\tparamerters num: 1024\n",
      "conv2.pointwise_conv.bias\tparamerters num: 32\n",
      "conv3.depthwise_conv.weight\tparamerters num: 288\n",
      "conv3.pointwise_conv.weight\tparamerters num: 2048\n",
      "conv3.pointwise_conv.bias\tparamerters num: 64\n",
      "conv4.depthwise_conv.weight\tparamerters num: 576\n",
      "conv4.pointwise_conv.weight\tparamerters num: 4096\n",
      "conv4.pointwise_conv.bias\tparamerters num: 64\n",
      "conv5.depthwise_conv.weight\tparamerters num: 576\n",
      "conv5.pointwise_conv.weight\tparamerters num: 8192\n",
      "conv5.pointwise_conv.bias\tparamerters num: 128\n",
      "conv6.depthwise_conv.weight\tparamerters num: 1152\n",
      "conv6.pointwise_conv.weight\tparamerters num: 16384\n",
      "conv6.pointwise_conv.bias\tparamerters num: 128\n",
      "fc1.weight\tparamerters num: 147456\n",
      "fc1.bias\tparamerters num: 128\n",
      "fc2.weight\tparamerters num: 1280\n",
      "fc2.bias\tparamerters num: 10\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, activation=\"relu\"):\n",
    "        super(CNN, self).__init__()\n",
    "        self.activation = F.relu if activation == \"relu\" else F.selu\n",
    "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=\"same\")\n",
    "        self.conv2 = DepthWiseConv2d(in_channels=32, out_channels=32, kernel_size=3, padding=\"same\")\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv3 = DepthWiseConv2d(in_channels=32, out_channels=64, kernel_size=3, padding=\"same\")\n",
    "        self.conv4 = DepthWiseConv2d(in_channels=64, out_channels=64, kernel_size=3, padding=\"same\")\n",
    "        self.conv5 = DepthWiseConv2d(in_channels=64, out_channels=128, kernel_size=3, padding=\"same\")\n",
    "        self.conv6 = DepthWiseConv2d(in_channels=128, out_channels=128, kernel_size=3, padding=\"same\")\n",
    "        self.flatten = nn.Flatten()\n",
    "        # input shape is (28, 28, 1) so the fc1 layer in_features is 128 * 3 * 3\n",
    "        self.fc1 = nn.Linear(128 * 3 * 3, 128)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "        \n",
    "        self.init_weights()\n",
    "        \n",
    "    def init_weights(self):\n",
    "        \"\"\"使用 xavier 均匀分布来初始化全连接层、卷积层的权重 W\"\"\"\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, (nn.Linear, nn.Conv2d)):\n",
    "                nn.init.xavier_uniform_(m.weight)\n",
    "                if m.bias is not None:\n",
    "                    nn.init.zeros_(m.bias)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        act = self.activation\n",
    "        #x -->(batch_size, 1, 28, 28)\n",
    "        x = self.pool(act(self.conv2(act(self.conv1(x)))))  # (batch_size, 32, 14, 14)\n",
    "        x = self.pool(act(self.conv4(act(self.conv3(x)))))  # (batch_size, 64, 7, 7)\n",
    "        x = self.pool(act(self.conv6(act(self.conv5(x)))))  # (batch_size, 128, 3, 3)\n",
    "        x = self.flatten(x) # (batch_size, 128 * 3 * 3)\n",
    "        x = act(self.fc1(x)) # (batch_size, 128)\n",
    "        x = self.fc2(x) # (batch_size, 10)\n",
    "        return x\n",
    "    \n",
    "\n",
    "for idx, (key, value) in enumerate(CNN().named_parameters()):\n",
    "    print(f\"{key}\\tparamerters num: {np.prod(value.shape)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 总权重数 = 288 + 288 + 1024 + 2048 + 576 + 8192 + 1152 + 16384 + 147456 + 1280 = 186624\n",
    "# 总偏置数 = 32 + 32 + 64 + 64 + 128 + 128 + 10 = 438\n",
    "#\n",
    "# 整个网络的总参数数为：\n",
    "#\n",
    "# 总参数数 = 总权重数 + 总偏置数 = 186624 + 438 = 187062"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练\n",
    "\n",
    "pytorch的训练需要自行实现，包括\n",
    "1. 定义损失函数\n",
    "2. 定义优化器\n",
    "3. 定义训练步\n",
    "4. 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    pred_list = []\n",
    "    label_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        \n",
    "        preds = logits.argmax(axis=-1)    # 验证集预测\n",
    "        pred_list.extend(preds.cpu().numpy().tolist())\n",
    "        label_list.extend(labels.cpu().numpy().tolist())\n",
    "        \n",
    "    acc = accuracy_score(label_list, pred_list)\n",
    "    return np.mean(loss_list), acc\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorBoard 可视化\n",
    "\n",
    "\n",
    "训练过程中可以使用如下命令启动tensorboard服务。\n",
    "\n",
    "```shell\n",
    "tensorboard \\\n",
    "    --logdir=runs \\     # log 存放路径\n",
    "    --host 0.0.0.0 \\    # ip\n",
    "    --port 8848         # 端口\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "class TensorBoardCallback:\n",
    "    def __init__(self, log_dir, flush_secs=10):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            log_dir (str): dir to write log.\n",
    "            flush_secs (int, optional): write to dsk each flush_secs seconds. Defaults to 10.\n",
    "        \"\"\"\n",
    "        self.writer = SummaryWriter(log_dir=log_dir, flush_secs=flush_secs)\n",
    "\n",
    "    def draw_model(self, model, input_shape):\n",
    "        self.writer.add_graph(model, input_to_model=torch.randn(input_shape))\n",
    "        \n",
    "    def add_loss_scalars(self, step, loss, val_loss):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/loss\", \n",
    "            tag_scalar_dict={\"loss\": loss, \"val_loss\": val_loss},\n",
    "            global_step=step,\n",
    "            )\n",
    "        \n",
    "    def add_acc_scalars(self, step, acc, val_acc):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/accuracy\",\n",
    "            tag_scalar_dict={\"accuracy\": acc, \"val_accuracy\": val_acc},\n",
    "            global_step=step,\n",
    "        )\n",
    "        \n",
    "    def add_lr_scalars(self, step, learning_rate):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/learning_rate\",\n",
    "            tag_scalar_dict={\"learning_rate\": learning_rate},\n",
    "            global_step=step,\n",
    "            \n",
    "        )\n",
    "    \n",
    "    def __call__(self, step, **kwargs):\n",
    "        # add loss\n",
    "        loss = kwargs.pop(\"loss\", None)\n",
    "        val_loss = kwargs.pop(\"val_loss\", None)\n",
    "        if loss is not None and val_loss is not None:\n",
    "            self.add_loss_scalars(step, loss, val_loss)\n",
    "        # add acc\n",
    "        acc = kwargs.pop(\"acc\", None)\n",
    "        val_acc = kwargs.pop(\"val_acc\", None)\n",
    "        if acc is not None and val_acc is not None:\n",
    "            self.add_acc_scalars(step, acc, val_acc)\n",
    "        # add lr\n",
    "        learning_rate = kwargs.pop(\"lr\", None)\n",
    "        if learning_rate is not None:\n",
    "            self.add_lr_scalars(step, learning_rate)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        \"\"\"\n",
    "        Save checkpoints each save_epoch epoch. \n",
    "        We save checkpoint by epoch in this implementation.\n",
    "        Usually, training scripts with pytorch evaluating model and save checkpoint by step.\n",
    "\n",
    "        Args:\n",
    "            save_dir (str): dir to save checkpoint\n",
    "            save_epoch (int, optional): the frequency to save checkpoint. Defaults to 1.\n",
    "            save_best_only (bool, optional): If True, only save the best model or save each model at every epoch.\n",
    "        \"\"\"\n",
    "        self.save_dir = save_dir\n",
    "        self.save_step = save_step\n",
    "        self.save_best_only = save_best_only\n",
    "        self.best_metrics = -1\n",
    "        \n",
    "        # mkdir\n",
    "        if not os.path.exists(self.save_dir):\n",
    "            os.mkdir(self.save_dir)\n",
    "        \n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return\n",
    "        \n",
    "        if self.save_best_only:\n",
    "            assert metric is not None\n",
    "            if metric >= self.best_metrics:\n",
    "                # save checkpoints\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\"))\n",
    "                # update best metrics\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 20:45:50.148376: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b44327980f64325abc5e7aede047398",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/34380 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                preds = logits.argmax(axis=-1)\n",
    "            \n",
    "                acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())    \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"acc\": acc, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss, val_acc = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"acc\": val_acc, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "                    \n",
    "                    # 1. 使用 tensorboard 可视化\n",
    "                    if tensorboard_callback is not None:\n",
    "                        tensorboard_callback(\n",
    "                            global_step, \n",
    "                            loss=loss, val_loss=val_loss,\n",
    "                            acc=acc, val_acc=val_acc,\n",
    "                            lr=optimizer.param_groups[0][\"lr\"],\n",
    "                            )\n",
    "                    \n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=val_acc)\n",
    "\n",
    "                    # 3. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(val_acc)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 20\n",
    "\n",
    "activation = \"selu\"\n",
    "model = CNN(activation)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = nn.CrossEntropyLoss()\n",
    "# 2. 定义优化器 采用SGD\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# 1. tensorboard 可视化\n",
    "if not os.path.exists(\"runs\"):\n",
    "    os.mkdir(\"runs\")\n",
    "tensorboard_callback = TensorBoardCallback(f\"runs/dsc-{activation}\")\n",
    "tensorboard_callback.draw_model(model, [1, 1, 28, 28])\n",
    "# 2. save best\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(f\"checkpoints/dsc-{activation}\", save_step=len(train_loader), save_best_only=True)\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=10)\n",
    "\n",
    "model = model.to(device)\n",
    "record = training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=tensorboard_callback,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    fig_num = len(train_df.columns)\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(5 * fig_num, 5))\n",
    "    for idx, item in enumerate(train_df.columns):    \n",
    "        axs[idx].plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        axs[idx].grid()\n",
    "        axs[idx].legend()\n",
    "        axs[idx].set_xticks(range(0, train_df.index[-1], 5000))\n",
    "        axs[idx].set_xticklabels(map(lambda x: f\"{int(x/1000)}k\", range(0, train_df.index[-1], 5000)))\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves(record, sample_step=500)  #横坐标是 steps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3736\n",
      "accuracy: 0.8625\n"
     ]
    }
   ],
   "source": [
    "# dataload for evaluating\n",
    "\n",
    "# load checkpoints\n",
    "model.load_state_dict(torch.load(f\"checkpoints/dsc-{activation}/best.ckpt\", map_location=\"cpu\"))\n",
    "\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, test_loader, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ]
  }
 ],
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